Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Adaptively choosing niching parameters in a PSO
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multi-Species Particle Swarm Optimizer for Multimodal Function Optimization
IEICE - Transactions on Information and Systems
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Particle swarm optimization for multimodal functions: a clustering approach
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Gravitational interactions optimization
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
A new fitness estimation strategy for particle swarm optimization
Information Sciences: an International Journal
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The problem of finding more than one optimum of a fitness function has been addressed in evolutionary computation using a wide variety of algorithms, including particle swarm optimization (PSO). Several variants of the PSO algorithm have been developed to deal with this sort of problem with different degrees of success, but a common drawback of such approaches is that they normally add new parameters that need to be properly tuned, and whose values usually rely on previous knowledge of the fitness function being analyzed. In this paper, we present a PSO algorithm based on electrostatic interaction, which does not need any additional parameters besides those of the original PSO. We show that our proposed approach is able to converge to all the optima of several test functions commonly adopted in the specialized literature, consuming less evaluations of the fitness function than other previously reported PSO methods.